Turn recordings
into clean, readable text.

A different approach to clean verbatim transcription.

Clean verbatim transcription is the practice of transcribing spoken audio word for word, then removing the filler words, false starts, and stutters that clutter a raw transcript, without cutting anything a speaker actually meant. Researchers, legal teams, journalists, and accessibility teams have used it for years to turn recordings into a document people can actually read, cite, and search.

Why raw transcripts break down

Most teams do not get to choose between verbatim and clean verbatim by design. They get whatever the transcription tool hands them: a wall of “um,” “uh,” and half-finished sentences, or a version so smoothed over that a quote gets rewritten into something the speaker never said. Reviewers spend hours cleaning transcripts by hand before they are usable in a report, a brief, or a caption file, and the edits are not consistent from one transcriber to the next.

Reading the recording, not just the words

Speak AI transcribes every recording in your language, with 100+ supported, and applies clean verbatim formatting as a rule, not a one-off edit: filler words and false starts removed, punctuation and paragraph breaks added for readability, while the speaker’s actual words, arguments, and admissions stay untouched. Because Speak AI also reads the recording itself and not just the transcript, it captures the tone, emotion, and energy behind what was said, alongside the words, so a clean transcript never loses what a raw one would have shown.

What clean verbatim keeps, and what it drops

The line is simple in principle and easy to get wrong in practice. Clean verbatim:

• Removes standalone filler words like “um,” “uh,” and “you know” when they carry no meaning.
• Cleans false starts. “I think we should— we should move forward with the project” becomes “I think we should move forward with the project.”
• Keeps stutters and repetitions when they change the meaning or are part of a record being quoted, such as a witness statement or a clinical note.
• Standardizes grammar, punctuation, and spelling for readability, never for what was actually said.

From a raw recording to a citable transcript

The result is a transcript your team can actually use: readable enough to drop into a report or a caption file, accurate enough to quote in a brief or a paper. Ask across your entire transcript library with AI chat, using the same prompt workflows teams once stitched together by hand, now running natively over your recordings with ChatGPT, Claude, and Gemini built in. Trends across hundreds of interviews or hearings become dashboards you can customize and white-label, tracking themes and speaker sentiment over time, so this quarter’s interviews are measured against last quarter’s. One legal intelligence firm put its call review through this workflow and processed 5,100+ hours and saved $700K, without adding headcount. And because clean verbatim rarely stands alone, the same engine scores calls, meetings, and interviews on the same criteria, connecting the transcript to call scoring and reachable from Claude, ChatGPT, and Cursor through the MCP server.